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Pediatric traumatic brain injury (TBI) is a significant health concern, yet access to rehabilitation therapies for children remains limited. Environmental enrichment (EE) is a preclinical model of neurorehabilitation that promotes behavioral recovery and reduces neuroinflammation after TBI. While the gut microbiota has recently emerged as a potential therapeutic target for treating TBI sequelae in adults, its role in recovery after pediatric TBI remains unclear. Therefore, our aim was to assess the effect of EE on gut microbiota and its correlation with cognition as well as microglial morphology in a preclinical model of pediatric TBI. Male rats underwent a controlled cortical impact of moderate severity or sham injury at postnatal day 21 and were then randomly assigned to either EE or standard (STD) housing. Cognition was evaluated using the Morris water maze (MWM) on post-injury days 14-19. Microglial morphology and caecum microbiota was characterized on post-injury day 21. Cognitive deficits and increased microglial activation in the ipsilateral cortex were observed in the STD-housed TBI rats but not those in EE. TBI decreased microbiota α-diversity, while PERMANOVA analysis showed that both TBI and EE modified microbiota β-diversity. Furthermore, regression models indicated that microglial morphology in the ipsilateral cortex and Lactobacillus reuteri predicted behavioral outcomes, while Prevotellaceae NK3B31 was associated with microglial morphology. The data suggest that EE mitigates TBI-induced alterations in gut microbiota and that there is a complex interplay between EE, microbiota and microglial morphology that predicts behavioral recovery in pediatric rats., Competing Interests: Declaration of competing interest None., (Copyright © 2024. Published by Elsevier Inc.)

The Critical Assessment of Massive Data Analysis (CAMDA) addresses the complexities of harnessing Big Data in life sciences by hosting annual competitions that inspire research groups to develop innovative solutions. In 2023, the Forensic Challenge focused on identifying the city of origin for 365 metagenomic samples collected from public transportation systems and identifying associations between bacterial distribution and other covariates. For microbiome classification, we incorporated both taxonomic and functional annotations as features. To identify the most informative Operational Taxonomic Units, we selected features by fitting negative binomial models. We then implemented supervised models conducting 5-fold cross-validation (CV) with a 4:1 training-to-validation ratio. After variable selection, which reduced the dataset to fewer than 300 OTUs, the Support Vector Classifier achieved the highest F1 score (0.96). When using functional features from MIFASER, the Neural Network model outperformed other models. When considering climatic and demographic variables of the cities, Dirichlet regression over Escherichia , Enterobacter , and Klebsiella bacteria abundances suggests that population increase is indeed associated with a rise in the mean of Escherichia while decreasing temperature is linked to higher proportions of Klebsiella . This study validates microbiome classification using taxonomic features and, to a lesser extent, functional features. It shows that demographic and climatic factors influence urban microbial distribution. A Docker container and a Conda environment are available at the repository: GitHub facilitating broader adoption and validation of these methods by the scientific community., Competing Interests: The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Contreras-Peruyero, Nuñez, Vazquez-Rosas-Landa, Santana-Quinteros, Pashkov, Carranza-Barragán, Perez-Estrada, Guerrero-Flores, Balanzario, Muñiz Sánchez, Nakamura, Ramírez-Ramírez and Sélem-Mojica.)

The Actinomycetota (formerly Actinobacteria) genus Clavibacter includes phytopathogens with devasting effects in several crops. Clavibacter michiganensis , the causal agent of tomato bacterial canker, is the most notorious species of the genus. Yet, its origin and natural reservoirs remain elusive, and its populations show pathogenicity profiles with unpredictable plant disease outcomes. Here, we generate and analyse a decade-long genomic dataset of Clavibacter from wild and commercial tomato cultivars, providing evolutionary insights that directed phenotypic characterization. Our phylogeny situates the last common ancestor of C. michiganensis next to Clavibacter isolates from grasses rather than to the sole strain we could isolate from wild tomatoes. Pathogenicity profiling of C. michiganensis isolates, together with C. phaseoli and C. californiensis as sister taxa and the wild tomato strain, was found to be congruent with the proposed phylogenetic relationships. We then identified gene enrichment after the evolutionary event, leading to the appearance of the C. michiganesis clade, including known pathogenicity factors but also hitherto unnoticed genes with the ability to encode adaptive traits for a pathogenic lifestyle. The holistic perspective provided by our evolutionary analyses hints towards a host shift event as the origin of C. michiganensis as a tomato pathogen and the existence of pathogenic genes that remain to be characterized.

Motivation: As genomics data analysis becomes increasingly intricate, researchers face the challenge of mastering various software tools. The rise of Pangenomics analysis, which examines the complete set of genes in a group of genomes, is particularly transformative in understanding genetic diversity. Our interdisciplinary team of biologists and mathematicians developed a short Pangenomics Workshop covering Bash, Python scripting, Pangenome, and Topological Data Analysis. These skills provide deeper insights into genetic variations and their implications in Evolutionary Biology. The workshop uses a Conda environment for reproducibility and accessibility. Developed in The Carpentries Incubator infrastructure, the workshop aims to equip researchers with essential skills for Pangenomics research. By emphasizing the role of a community of practice, this work underscores its significance in empowering multidisciplinary professionals to collaboratively develop training that adheres to best practices., Results: Our workshop delivers tangible outcomes by enhancing the skill sets of Computational Biology professionals. Participants gain hands-on experience using real data from the first described pangenome. We share our paths toward creating an open-source, multidisciplinary, and public resource where learners can develop expertise in Pangenomic Analysis. This initiative goes beyond advancing individual capabilities, aligning with the broader mission of addressing educational needs in Computational Biology., Availability and Implementation: https://carpentries-incubator.github.io/pangenomics-workshop/., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press.)

Motivation: Wikipedia is a vital open educational resource in computational biology. The quality of computational biology coverage in English-language Wikipedia has improved steadily in recent years. However, there is an increasingly large 'knowledge gap' between computational biology resources in English-language Wikipedia, and Wikipedias in non-English languages. Reducing this knowledge gap by providing educational resources in non-English languages would reduce language barriers which disadvantage non-native English speaking learners across multiple dimensions in computational biology., Results: Here, we provide a comprehensive assessment of computational biology coverage in Spanish-language Wikipedia, the second most accessed Wikipedia worldwide. Using Spanish-language Wikipedia as a case study, we generate quantitative and qualitative data before and after a targeted educational event, specifically, a Spanish-focused student editing competition. Our data demonstrates how such events and activities can narrow the knowledge gap between English and non-English educational resources, by improving existing articles and creating new articles. Finally, based on our analysis, we suggest ways to prioritize future initiatives to improve open educational resources in other languages., Availability and Implementation: Scripts for data analysis are available at: https://github.com/ISCBWikiTeam/spanish., (© The Author(s) 2024. Published by Oxford University Press.)

Natural products from Actinomycetota have served as inspiration for many clinically relevant therapeutics. Despite early triumphs in natural product discovery, the rate of unearthing new compounds has decreased, necessitating inventive approaches. One promising strategy is to explore environments where survival is challenging. These harsh environments are hypothesized to lead to bacteria developing chemical adaptations (e.g. natural products) to enable their survival. This investigation focuses on ore-forming environments, particularly fluoride mines, which typically have extreme pH, salinity and nutrient scarcity. Herein, we have utilized metagenomics, metabolomics and evolutionary genome mining to dissect the biodiversity and metabolism in these harsh environments. This work has unveiled the promising biosynthetic potential of these bacteria and has demonstrated their ability to produce bioactive secondary metabolites. This research constitutes a pioneering endeavour in bioprospection within fluoride mining regions, providing insights into uncharted microbial ecosystems and their previously unexplored natural products.

The natural products (NPs) biosynthetic gene clusters (BGCs) represent the adapting biochemical toolkit for microorganisms to thrive different microenvironments. Despite their high diversity, particularly at the genomic level, detecting them in a shake-flask is challenging and remains the primary obstacle limiting our access to valuable chemicals. Studying the molecular mechanisms that regulate BGC expression is crucial to design of artificial conditions that derive on their expression. Here, we propose a phylogenetic analysis of regulatory elements linked to biosynthesis gene clusters, to classify BGCs to regulatory mechanisms based on protein domain information. We utilized Hidden Markov Models from the Pfam database to retrieve regulatory elements, such as histidine kinases and transcription factors, from BGCs in the MIBiG database, focusing on actinobacterial strains from three distinct environments: oligotrophic basins, rainforests, and marine environments. Despite the environmental variations, our isolated microorganisms share similar regulatory mechanisms, suggesting the potential to activate new BGCs using activators known to affect previously characterized BGCs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Rodriguez-Sanchez, Gónzalez-Salazar, Rodriguez-Orduña, Cumsille, Undabarrena, Camara, Sélem-Mojica and Licona-Cassani.)

Actinobacteria is an ancient phylum of Gram-positive bacteria with a characteristic high GC content to their DNA. The ActinoBase Wiki is focused on the filamentous actinobacteria, such as Streptomyces species, and the techniques and growth conditions used to study them. These organisms are studied because of their complex developmental life cycles and diverse specialised metabolism which produces many of the antibiotics currently used in the clinic. ActinoBase is a community effort that provides valuable and freely accessible resources, including protocols and practical information about filamentous actinobacteria. It is aimed at enabling knowledge exchange between members of the international research community working with these fascinating bacteria. ActinoBase is an anchor platform that underpins worldwide efforts to understand the ecology, biology and metabolic potential of these organisms. There are two key differences that set ActinoBase apart from other Wiki-based platforms: [1] ActinoBase is specifically aimed at researchers working on filamentous actinobacteria and is tailored to help users overcome challenges working with these bacteria and [2] it provides a freely accessible resource with global networking opportunities for researchers with a broad range of experience in this field.

Last year ActinoBase, a Wiki-style initiative supported by the UK Microbiology Society, published a review highlighting the research of particular interest to the actinomycete community. Here, we present the second ActinoBase review showcasing selected reports published in 2020 and early 2021, integrating perspectives in the actinomycete field. Actinomycetes are well-known for their unsurpassed ability to produce specialised metabolites, of which many are used as therapeutic agents with antibacterial, antifungal, or immunosuppressive activities. Much research is carried out to understand the purpose of these metabolites in the environment, either within communities or in host interactions. Moreover, many efforts have been placed in developing computational tools to handle big data, simplify experimental design, and find new biosynthetic gene cluster prioritisation strategies. Alongside, synthetic biology has provided advances in tools to elucidate the biosynthesis of these metabolites. Additionally, there are still mysteries to be uncovered in understanding the fundamentals of filamentous actinomycetes' developmental cycle and regulation of their metabolism. This review focuses on research using integrative methodologies and approaches to understand the bigger picture of actinomycete biology, covering four research areas: i ) technology and methodology; ii ) specialised metabolites; iii ) development and regulation; and iv ) ecology and host interactions.

Natural products (NPs), or specialized metabolites, are important for medicine and agriculture alike, and for the fitness of the organisms that produce them. NP genome-mining aims at extracting biosynthetic information from the genomes of microbes presumed to produce these compounds. Typically, canonical enzyme sequences from known biosynthetic systems are identified after sequence similarity searches. Despite this being an efficient process, the likelihood of identifying truly novel systems by this approach is low. To overcome this limitation, we previously introduced EvoMining, a genome-mining approach that incorporates evolutionary principles. Here, we release and use our latest EvoMining version, which includes novel visualization features and customizable databases, to analyse 42 central metabolic enzyme families (EFs) conserved throughout Actinobacteria, Cyanobacteria, Pseudomonas and Archaea. We found that expansion-and-recruitment profiles of these 42 families are lineage specific, opening the metabolic space related to 'shell' enzymes. These enzymes, which have been overlooked, are EFs with orthologues present in most of the genomes of a taxonomic group, but not in all. As a case study of canonical shell enzymes, we characterized the expansion and recruitment of glutamate dehydrogenase and acetolactate synthase into scytonemin biosynthesis, and into other central metabolic pathways driving Archaea and Bacteria adaptive evolution. By defining the origin and fate of enzymes, EvoMining complements traditional genome-mining approaches as an unbiased strategy and opens the door to gaining insights into the evolution of NP biosynthesis. We anticipate that EvoMining will be broadly used for evolutionary studies, and for generating predictions of unprecedented chemical scaffolds and new antibiotics. This article contains data hosted by Microreact.

Cycads are the only early seed plants that have evolved a specialized root to host endophytic bacteria that fix nitrogen. To provide evolutionary and functional insights into this million-year old symbiosis, we investigate endophytic bacterial sub-communities isolated from coralloid roots of species from Dioon (Zamiaceae) sampled from their natural habitats. We employed a sub-community co-culture experimental strategy to reveal both predominant and rare bacteria, which were characterized using phylogenomics and detailed metabolic annotation. Diazotrophic plant endophytes, including Bradyrhizobium, Burkholderia, Mesorhizobium, Rhizobium, and Nostoc species, dominated the epiphyte-free sub-communities. Draft genomes of six cyanobacteria species were obtained after shotgun metagenomics of selected sub-communities. These data were used for whole-genome inferences that suggest two Dioon-specific monophyletic groups, and a level of specialization characteristic of co-evolved symbiotic relationships. Furthermore, the genomes of these cyanobacteria were found to encode unique biosynthetic gene clusters, predicted to direct the synthesis of specialized metabolites, mainly involving peptides. After combining genome mining with detection of pigment emissions using multiphoton excitation fluorescence microscopy, we also show that Caulobacter species co-exist with cyanobacteria, and may interact with them by means of a novel indigoidine-like specialized metabolite. We provide an unprecedented view of the composition of the cycad coralloid root, including phylogenetic and functional patterns mediated by specialized metabolites that may be important for the evolution of ancient symbiotic adaptations.